How Does the Rise of Systematic Internalisers Impact a Firm’s Best Execution Obligations and Analysis?

Concept

The emergence of the Systematic Internaliser (SI) regime under MiFID II represents a fundamental re-architecting of the European market structure, formalizing a practice that has long been a component of institutional trading ▴ principal-based liquidity provision. An SI is an investment firm which, on an organized, frequent, systematic, and substantial basis, deals on its own account when executing client orders outside a regulated market, a multilateral trading facility (MTF), or an organized trading facility (OTF). In essence, the SI framework brings a significant volume of over-the-counter (OTC) trading into a more transparent and regulated environment.

The firm acting as an SI is not a neutral venue; it is a counterparty, committing its own capital to complete a client’s trade. This direct, principal-based interaction distinguishes it from agency-based models where a broker routes orders to external venues.

This development directly intersects with a firm’s best execution obligations, which mandate taking all sufficient steps to obtain the best possible result for clients. The obligation is multifaceted, considering not just price, but also costs, speed, likelihood of execution and settlement, size, and any other relevant consideration. The rise of SIs introduces a powerful, yet complex, new variable into this equation. It provides a consolidated and often deep pool of liquidity that can be particularly valuable for large orders, potentially reducing market impact.

However, because the SI is the principal, the price formation mechanism is bilateral and opaque compared to the transparent, multilateral order books of lit exchanges. This creates a critical analytical challenge for buy-side firms ▴ how to demonstrably prove that the execution obtained from an SI was superior to, or at least as good as, what could have been achieved through other available liquidity sources.

The formalization of Systematic Internalisers compels firms to integrate a new class of principal liquidity into their best execution analysis, shifting the focus toward proving the quality of opaque, bilateral trades.

The core of the issue lies in the data and the analytical framework required to satisfy this proof. Best execution is an evidence-based obligation. Before the formal SI regime, dealing with a bank’s principal desk was often considered a purely OTC transaction, with fairness assessed based on prevailing market conditions. MiFID II, by creating the SI category and imposing pre-trade transparency (quoting) obligations and post-trade reporting (RTS 27) requirements, provides a structured data stream where one previously did not exist in a standardized form.

This data, while a significant step forward, presents its own challenges. RTS 27 reports, for instance, offer quarterly data on execution quality but can be difficult to normalize and compare against the continuous, tick-by-tick data from lit markets. The responsibility, therefore, falls squarely on the investment firm to build a robust internal methodology that can ingest, analyze, and compare these disparate data sets to justify its venue selection on a trade-by-trade basis. The SI is a source of potential execution quality, but accessing it without a sophisticated analytical overlay creates significant compliance risk.

The SI as a Liquidity Node

Viewing the market as a network of interconnected liquidity pools, SIs function as high-capacity, proprietary nodes. Unlike public exchanges (lit markets) or even dark pools (MTFs), which aggregate multilateral interest, an SI represents a curated source of bilateral liquidity. A large bank or market-making firm operating an SI leverages its own trading activity and inventory to provide quotes. This can result in significant advantages, particularly for certain types of orders.

• Size Improvement ▴ For block trades that would cause significant market impact on a lit exchange, an SI can internalize the risk and provide a single price for the entire order, preventing information leakage and adverse price movement.
• Price Improvement ▴ SIs often compete for order flow by offering prices that are better than the prevailing European Best Bid and Offer (EBBO). This price improvement is a key metric in best execution analysis, but it must be assessed against the total cost of the trade, including any implicit costs.
• Certainty of Execution ▴ When an SI provides a firm quote, there is a high degree of certainty that the trade will be executed at that price for the quoted size, which can be a critical factor for time-sensitive or risk-reducing trades.

However, this proprietary liquidity model also introduces unique considerations for a firm’s best execution framework. The prices quoted by an SI are not formed by open competition in the same way as a central limit order book. They are set by the SI itself, based on its own models, inventory, and risk appetite. This necessitates a shift in best execution analysis from simply observing the best available price on a public screen to actively evaluating the quality of a private, bilateral quote.

The firm must have the tools to capture the state of the broader market at the moment of execution to benchmark the SI’s offer effectively. Without this contextual data, it becomes impossible to determine if the offered price was truly the “best possible result” for the client.

Recalibrating the Best Execution Factors

The traditional factors of best execution ▴ price, cost, speed, and likelihood ▴ must be reinterpreted and weighted differently when SIs are a significant part of the execution strategy. The emphasis shifts from a purely price-centric view to a more holistic assessment of total execution quality.

Beyond the Public Quote

The concept of “price” is no longer a single, observable data point like the EBBO. With SIs, the analysis must incorporate:

• Level of Price Improvement ▴ Quantifying the exact amount of improvement over the EBBO.
• Quote Stability ▴ Assessing how long an SI’s quotes remain firm, especially during volatile periods.
• Re-quote Rates ▴ Tracking how often an SI re-quotes or rejects a trade request, which can indicate a lack of true liquidity.

The Implicit Cost Dimension

The “cost” factor expands beyond explicit commissions. A critical component of SI analysis is the measurement of implicit costs, which are often hidden:

• Market Impact ▴ Even though an SI internalizes a trade, the information that a large order is being worked can leak through the SI’s own hedging activities in the public markets. Sophisticated Transaction Cost Analysis (TCA) is required to detect this subtle impact.
• Adverse Selection ▴ This is the risk that the SI is only willing to fill “easy” trades (those with low short-term volatility) while rejecting more difficult or informed orders, which are then sent to the open market with a greater signaling risk. A firm’s analysis must track fill rates and rejection patterns to identify potential adverse selection from SI counterparties.

The rise of SIs, therefore, transforms best execution from a compliance checklist into a dynamic, quantitative, and counterparty-focused discipline. It forces an evolution in a firm’s technological and analytical capabilities, demanding a framework that can justify the use of opaque liquidity pools through rigorous, data-driven evidence.

Strategy

The integration of Systematic Internalisers into a firm’s execution strategy necessitates a fundamental shift from a passive, venue-agnostic approach to an active, liquidity-sourcing framework. The core strategic challenge is to leverage the benefits of SI liquidity ▴ namely size and potential price improvement ▴ while rigorously managing the risks associated with opacity and bilateral relationships. This requires a multi-layered strategy that encompasses smart order routing logic, dynamic counterparty analysis, and a sophisticated post-trade analytics program designed to prove best execution in a fragmented market.

A modern execution strategy must treat SIs not as a monolithic block of liquidity but as a collection of distinct counterparties, each with its own risk appetite, quoting behavior, and potential for information leakage. The first step is to move beyond a simple “top of book” routing decision. A smart order router (SOR) must be calibrated to understand the trade-offs between hitting a lit market bid, posting in a dark pool, and requesting a quote from an SI. This decision cannot be static; it must be informed by the specific characteristics of the order (size, urgency, liquidity of the instrument) and the real-time state of the market.

For example, a large, illiquid order might be best suited for an SI to minimize market impact, whereas a small, liquid order might achieve a better all-in cost on a lit exchange. The SOR’s logic must be configurable and transparent, allowing the trading desk to understand and justify why a particular route was chosen.

A robust strategy for SI engagement hinges on evolving the firm’s analytical capabilities to continuously score and rank SI performance, feeding this intelligence back into the pre-trade decision-making of the smart order router.

This pre-trade intelligence must be fueled by a powerful post-trade feedback loop. The strategy must involve the systematic collection and analysis of all execution data, with a particular focus on SI fills. This is where Transaction Cost Analysis (TCA) becomes a strategic tool rather than a simple reporting function. The TCA framework must be capable of benchmarking SI executions against a variety of metrics, not just the arrival price or the EBBO at the time of the trade.

It should measure price improvement, effective spread capture, and, critically, post-trade reversion. Post-trade reversion, or the tendency of a stock’s price to move back after a trade, can be an indicator of market impact or information leakage. A pattern of high reversion on trades filled by a particular SI might suggest that the SI’s hedging activities are signaling the firm’s intentions to the broader market, negating the initial price improvement. This data-driven approach allows the firm to rank its SI counterparties based on true execution quality and dynamically adjust its order routing preferences.

Developing a Counterparty Evaluation Framework

A cornerstone of the strategy is the development of a quantitative framework for evaluating and tiering SI counterparties. This is analogous to how firms manage credit risk with their banking partners; it requires a systematic and ongoing assessment of performance. This framework should be built on a foundation of granular data captured from every interaction with an SI.

The evaluation model should incorporate several key performance indicators (KPIs):

1. Fill Rate and Quality ▴ This goes beyond simply measuring the percentage of orders filled. It should analyze the fill rate by order size, liquidity profile, and market volatility. An SI that provides high fill rates for small, easy-to-execute orders but consistently rejects larger, more difficult trades may be offloading risk rather than providing genuine liquidity. The quality of the fill, measured by price improvement versus the EBBO, is a primary metric.
2. Quote Integrity and Latency ▴ The framework must track the “firmness” of an SI’s quotes. This involves measuring the frequency of re-quotes or “last look” rejections, where the SI backs away from its initial quote. High rejection rates undermine the reliability of the SI as a liquidity source. Additionally, measuring the latency ▴ the time between sending a request for quote (RFQ) and receiving a response ▴ is critical for understanding the efficiency of the connection and the SI’s technological capabilities.
3. Post-Trade Reversion Analysis ▴ As mentioned, this is a sophisticated but vital component. The analysis should measure price movement in the seconds and minutes following an execution. Consistently high reversion associated with a specific SI is a red flag for information leakage. The table below illustrates a simplified version of how this analysis might look.

This quantitative scoring system allows the firm to move from a relationship-based allocation of order flow to a data-driven one. It provides an objective basis for directing orders to the SIs that consistently deliver the best results and for holding all counterparties accountable to a high standard of execution quality.

Adapting the Smart Order Router for an SI World

The intelligence gathered from the counterparty evaluation framework must be directly integrated into the firm’s pre-trade technology, specifically the Smart Order Router (SOR). An SOR designed for a market with significant SI liquidity must be more sophisticated than a simple price-seeking algorithm.

Key SOR Capabilities

• Liquidity-Seeking Logic ▴ The SOR should be able to intelligently probe for liquidity. Instead of just sending an order to the venue with the best displayed price, it might first send an RFQ to a select group of top-tiered SIs, especially for larger orders. This “pinging” of SIs must be done carefully to avoid signaling the firm’s intentions too broadly.
• Dynamic Venue Ranking ▴ The SOR should ingest the scores from the counterparty evaluation framework. It should dynamically rank SIs and other venues based on historical performance for similar types of orders. This means the SOR’s preferred routing path for a large-cap, high-volume stock might be different from that for a mid-cap, less liquid name.
• Consideration of Indication of Interest (IOI) ▴ Many SIs provide non-firm IOIs to signal their interest in trading certain stocks. A sophisticated SOR can use this IOI data as an input, increasing the probability of routing an order to an SI that is actively looking to trade that instrument, thereby increasing the likelihood of a high-quality fill.

By making the SOR “smarter” and more aware of the nuances of SI liquidity, the firm can automate a significant portion of the best execution process. This frees up human traders to focus on the most complex, difficult-to-execute orders, where their market knowledge and experience can add the most value. The strategy becomes a symbiotic relationship between human oversight and automated, data-driven execution logic, all aimed at achieving and proving the best possible result for the client in a complex and fragmented market.

Execution

Executing a best execution policy in an environment populated by Systematic Internalisers is a matter of operationalizing a data-centric culture. It requires the precise implementation of technological systems, analytical methodologies, and governance procedures that transform the abstract obligation of “all sufficient steps” into a concrete, auditable, and defensible workflow. The process moves beyond high-level strategy to the granular details of data capture, metric calculation, and decision justification. At its core, this is an engineering challenge ▴ to build a robust execution framework that can systematically prove the value of every routing decision, particularly those directed toward opaque liquidity pools like SIs.

The foundation of this framework is a comprehensive data architecture. The firm must be able to capture and time-stamp, with millisecond precision, every piece of data relevant to an order’s lifecycle. This includes the initial order receipt, the state of all relevant market order books at the time of routing decisions, the RFQs sent to SIs, the quotes received, the final execution report, and the post-trade market data needed for reversion analysis.

Without this complete and synchronized data set, any subsequent analysis is fundamentally flawed. This data repository becomes the single source of truth for the entire best execution process, feeding into the TCA systems, the counterparty scorecards, and the compliance reporting modules.

The operational execution of a best execution policy in the age of SIs is achieved by constructing a rigorous, evidence-based system where post-trade analytics continuously refine pre-trade routing logic.

With the data architecture in place, the next layer is the analytical engine. This is where the firm implements the models for calculating the KPIs that drive the counterparty evaluation framework. This is not a one-time setup; it is a continuous process of model validation and refinement. The firm must be able to demonstrate to regulators why it chose specific benchmarks (e.g. arrival price, VWAP, EBBO), how it calculates metrics like price improvement and effective spread, and how it attributes market impact.

This requires a dedicated quantitative resource, either in-house or from a specialized vendor, to build and maintain these models. The output of this engine is not just a series of historical reports; it is actionable intelligence that is fed back into the pre-trade environment to improve future execution quality. The entire system creates a virtuous cycle ▴ trade, measure, analyze, and adapt.

A Procedural Guide to Integrating SIs into Best Execution

Successfully navigating the SI landscape requires a detailed, step-by-step operational plan. The following procedure outlines the key stages for integrating SIs into a firm’s best execution framework in a compliant and effective manner.

1. Policy and Governance Definition ▴
   • Update the Order Execution Policy ▴ Explicitly define the role of SIs as a distinct execution venue category. Detail the circumstances under which orders may be routed to SIs (e.g. based on order size, instrument liquidity, or as part of a competitive RFQ process).
   • Establish a Best Execution Committee ▴ Create a cross-functional committee with representation from trading, compliance, risk, and technology. This committee is responsible for overseeing the execution policy, reviewing TCA reports, approving SI counterparties, and documenting all decisions.
   • Define the Counterparty Onboarding Process ▴ Formalize the due diligence process for adding a new SI relationship. This should include an assessment of the SI’s financial stability, technological capabilities, and regulatory standing.
2. Pre-Trade Analysis and Routing Configuration ▴
   • Configure the Smart Order Router (SOR) ▴ Implement the dynamic venue ranking logic. The SOR must be programmed to incorporate the SI scorecard data, allowing it to prioritize SIs based on historical performance. Set rules for when to use RFQs versus sending passive or aggressive orders to lit markets.
   • Implement Pre-Trade TCA ▴ Before an order is sent to the market, a pre-trade TCA tool should provide an estimate of the expected execution cost and market impact across different potential venues, including SIs. This provides a baseline against which to measure the final execution.
   • Set Controls for Information Leakage ▴ Configure the RFQ process to manage signaling risk. This may involve limiting the number of SIs queried simultaneously or using a “waterfall” approach where the order is shown to SIs sequentially.
3. Execution and Data Capture ▴
   • Ensure High-Precision Time-Stamping ▴ All events in the order lifecycle must be time-stamped using a synchronized clock (e.g. PTP or NTP) to ensure data integrity for post-trade analysis. This is a critical MiFID II requirement.
   • Capture Full Depth of Book Data ▴ The system must capture not just the top-of-book (EBBO) but the full depth of the order book for all relevant lit markets at the time of execution. This provides the necessary context for evaluating the quality of an SI’s off-book quote.
   • Record SI Quote and Rejection Data ▴ Every quote received from an SI, along with any rejections or re-quotes, must be logged. This data is essential for calculating the quote integrity metrics in the counterparty scorecard.
4. Post-Trade Analysis and Reporting ▴
   • Run Daily TCA Reports ▴ The TCA system should automatically process the previous day’s trades and generate reports that detail execution performance by counterparty, strategy, and trader. These reports should be reviewed by the trading desk daily.
   • Conduct Quarterly Counterparty Reviews ▴ The Best Execution Committee must meet quarterly to review the SI scorecard (as illustrated in the Strategy section) and make formal decisions about the tiering and allocation of flow to each SI. All decisions and the data supporting them must be minuted.
   • Generate RTS 28 Reports ▴ The data collected and analyzed through this process provides the necessary inputs for the annual RTS 28 report, which requires firms to publish information on their top five execution venues (by volume) for each class of financial instrument and a summary of the analysis of the quality of execution obtained.

Quantitative Deep Dive Transaction Cost Analysis

The credibility of any best execution framework rests on the quantitative rigor of its TCA. The following table provides a more granular look at a TCA report for a single, large order, demonstrating how different metrics can be used to compare a hypothetical execution on an SI versus other potential venues. This level of detail is what is required to defend an execution decision to regulators and clients.

This type of analysis provides the evidence-based justification required under MiFID II. It demonstrates that the firm not only considered the explicit price but also modeled the implicit costs (market impact, information leakage) associated with different execution channels. By choosing SI Alpha, the firm can prove it took sufficient steps to secure the best possible outcome, balancing the need for price improvement with the critical goal of minimizing adverse market reaction. This systematic, data-driven, and well-documented process is the hallmark of a robust and compliant execution framework in the modern market structure.

Evolving the Execution Framework

The integration of Systematic Internalisers into the marketplace is a catalyst for introspection. It compels every institutional firm to examine the very architecture of its execution and analysis systems. The question moves from “Are we compliant?” to “Is our framework intelligent?” The data streams, routing protocols, and analytical models are components of a larger cognitive system designed to navigate liquidity. The presence of SIs introduces a new set of pathways and decision points within that system.

How does your firm’s current infrastructure process and learn from the feedback generated by these bilateral liquidity sources? Is your post-trade analysis merely a report card on past performance, or is it a dynamic input that actively refines and improves the logic of your pre-trade decisions? The challenge is to build a framework that not only sees the market as it is but also anticipates the consequences of its own actions within it. The ultimate advantage lies in constructing an execution system that is not just robust, but genuinely adaptive.